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图学学报

图学学报 ›› 2020, Vol. 41 ›› Issue (6): 962-969.DOI: 10.11996/JG.j.2095-302X.2020060962

• 计算机图形学与虚拟现实 • 上一篇    下一篇

基于改进 L-K 光流的 WebAR 信息可视分析方法

  

  1. (1. 西南科技大学计算机科学与技术学院，四川 绵阳 621010； 2. 四川轻化工大学计算机科学与工程学院，四川 自贡 643002； 3. 法政大学计算机信息科学学院，日本 东京 184-8584)
  • 出版日期:2020-12-31 发布日期:2021-01-08
  • 基金资助:
    基金项目：四川省重点研发计划项目(2020YFS0360)；国家自然科学基金项目(61872304，61802320，61872066，61502083)；四川省教育厅科技创 新团队支持计划(18zd1102)  

IV LKWA: an information visual analysis tool with advanced L-K optical flow based WebAR 

  1. (1. School of Computer Science & Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China; 2. School of Computer Science & Engineering, Sichuan University of Science and Engineering, Zigong Sichuan 643002, China; 3. Graduate School of Computer Information Science, Hosei University, Tokyo 184-8584, Japan) 
  • Online:2020-12-31 Published:2021-01-08
  • Supported by:
    Foundation items：Key Research and Development Project of Sichuan Province (2020YFS0360); National Natural Science Foundation of China (61872304, 61802320, 61872066, 61502083); Program for Innovation Team of Sichuan Province Committee of China (18zd1102) 

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摘要： 摘 要：信息可视化技术结合移动增强现实(MAR)技术在目标跟踪领域仍然存在设备计算 负载过大的问题。若仍坚持采用同跟踪平面图像特征点的方案来跟踪立体对象各角度的特征点， 则目标跟踪过程所需要获取的多角度特征点数据无疑会加重跟踪过程的计算压力，进而导致移 动设备负载过大，最终影响模型渲染，所渲染的模型常出现剧烈抖动、卡顿或运动滞后于目标 物的现象。针对上述问题，提出了一种基于改进的 L-K (Lucas-Kanade)光流跟踪算法的 WebAR (基于 Web 端的 MAR 技术)解决方案，将特征点的跟踪问题转化为光流估计问题以及一种优化 的三维信息可视化交互策略。实验结果表明，该方法能够提高 MAR 在跟踪目标时的计算效率 和稳定性，丰富信息可视化的呈现效果和交互方式。

关键词: 关 键 词：移动增强现实, WebAR, 信息可视化, Lucas-Kanade 算法, 光流跟踪

Abstract: Abstract: For the mobile augmented reality (MAR) technology combined with information visualization, there remains excessive computing pressure on devices in target tracking. If the MAR application adopts the feature tracking solution for 2D images to track 3D objects, the multi-angle feature points in 3D objects obtained from extensive calculations will undoubtedly increase computing pressure in tracking process. As a consequence, excessive computing pressure will be incurred on devices, leading to unstable phenomena in the scene, such as jitter, latency, and movement of 3D AR model lagging behind the target during the target tracking. To resolve these problems, a WebAR (web-based AR) solution was proposed based on the advanced L-K method (Lucas Kanade method, an optical flow algorithm), which transformed the feature point tracking problem into an optical flow estimation problem and an optimized 3D interaction strategy for information visualization. The experimental results could verify that the proposed method can effectively enhance the computing efficiency and stability of target tracking in MAR and enrich the presentation and interaction in information visualization with WebAR.

Key words: Keywords: mobile augmented reality, WebAR, information visualization, Lucas-Kanade method; optical flow 

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